8 citations · 13 across the 3 of their papers we have counts for
6 papers
Engineering Sketch Generation for Computer-Aided Design
Karl D. D. Willis, Pradeep Kumar Jayaraman, Joseph G. Lambourne +2
Engineering sketches form the 2D basis of parametric Computer-Aided Design (CAD), the foremost modeling paradigm for manufactured objects. In this paper we tackle the problem of le…
BRepNet: A topological message passing system for solid models
Joseph G. Lambourne, Karl D. D. Willis, Pradeep Kumar Jayaraman +3
Boundary representation (B-rep) models are the standard way 3D shapes are described in Computer-Aided Design (CAD) applications. They combine lightweight parametric curves and surf…
RobustPointSet: A Dataset for Benchmarking Robustness of Point Cloud Classifiers
Saeid Asgari Taghanaki, Jieliang Luo, Ran Zhang +3
The 3D deep learning community has seen significant strides in pointcloud processing over the last few years. However, the datasets on which deep models have been trained have larg…
PointMask: Towards Interpretable and Bias-Resilient Point Cloud Processing
Saeid Asgari Taghanaki, Kaveh Hassani, Pradeep Kumar Jayaraman +2
Deep classifiers tend to associate a few discriminative input variables with their objective function, which in turn, may hurt their generalization capabilities. To address this, o…
UV-Net: Learning from Boundary Representations
Pradeep Kumar Jayaraman, Aditya Sanghi, Joseph G. Lambourne +4
We introduce UV-Net, a novel neural network architecture and representation designed to operate directly on Boundary representation (B-rep) data from 3D CAD models. The B-rep forma…
How Powerful Are Randomly Initialized Pointcloud Set Functions?
Aditya Sanghi, Pradeep Kumar Jayaraman
We study random embeddings produced by untrained neural set functions, and show that they are powerful representations which well capture the input features for downstream tasks su…